1.School of Communication and Information Engineering,Chongqing University of Posts and Telecommunications,Chongqing 400065,China 2.Research Center of New Communication Technology Applications,Chongqing University of Posts and Telecommunications,Chongqing 400065,China
About author:YUAN Quan, born in 1976, M. S., senior engineer. His research interests include big data, data mining, natural language processing. TANG Chengliang, born in 1997, M. S. candidate. His research interests include data mining. XU Yunpeng, born in 1998, M. S. candidate. His research interests include data mining.
Quan YUAN, Chengliang TANG, Yunpeng XU. Bat algorithm for high utility itemset mining based on length constraint[J]. Journal of Computer Applications, 2023, 43(5): 1473-1480.
LUNA J M, FOURNIER-VIGER P, VENTURA S. Frequent itemset mining: a 25 years review[J]. WIREs Data Mining and Knowledge Discovery, 2019, 9(6): No.e1329. 10.1002/widm.1329
2
YAO H, HAMILTON H J, GENG L. A unified framework for utility-based measures for mining itemset[C]// Proceeding of the 2nd Workshop on Utility-Based Data Mining Held in Conjunction with the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM, 2006: 28-37.
3
AHMED C F, TANBEER S K, JEONG B S, et al. Efficient tree structures for high utility pattern mining in incremental databases[J]. IEEE Transactions on Knowledge and Data Engineering, 2009, 21(12): 1708-1721. 10.1109/tkde.2009.46
4
TSENG V S, SHIE B E, WU C W, et al. Efficient algorithms for mining high utility itemsets from transactional databases[J]. IEEE Transactions on Knowledge and Data Engineering, 2013, 25(8): 1772-1786. 10.1109/tkde.2012.59
5
WU J M T, SRIVASTAVA G, LIN J C W, et al. Mining of high-utility patterns in big IoT-based databases[J]. Mobile Networks and Applications, 2021, 26(1): 216-233. 10.1007/s11036-020-01701-5
6
FOURNIER-VIGER P, LIN J C W, WU C W, et al. Mining minimal high-utility itemsets[C]// Proceeding of the 2016 International Conference on Database and Expert Systems Applications, LNCS 9827. Cham: Springer, 2016: 88-101.
7
FOURNIER-VIGER P, LIN J C W, DUONG Q H, et al. FHM+: faster high-utility itemset mining using length upper-bound reduction[C]// Proceeding of the 2016 International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, LNCS 9799. Cham: Springer, 2016: 115-127.
8
FOURNIER-VIGER P, WU C W, ZIDA S, et al. FHM: faster high-utility itemset mining using estimated utility co-occurrence pruning[C]// Proceeding of the 2014 International Symposium on Methodologies for Intelligent Systems, LNCS 8502. Cham: Springer, 2014: 83-92.
9
KANNIMUTHU S, PREMALATHA K. Discovery of high utility itemsets using genetic algorithm with ranked mutation[J]. Applied Artificial Intelligence, 2014, 28(4): 337-359. 10.1080/08839514.2014.891839
10
LIN J C W, YANG L, FOURNIER-VIGER P, et al. A binary PSO approach to mine high-utility itemsets[J]. Soft Computing, 2017, 21(17): 5103-5121. 10.1007/s00500-016-2106-1
WANG C W, YIN S L, LIU W Y, et al. High utility itemset mining algorithm based on improved particle swarm optimization[J]. Journal of Chinese Computer Systems, 2020, 41(5):1084-1090. 10.3969/j.issn.1000-1220.2020.05.031
12
SONG W, HUANG C M. Discovering high utility itemsets based on the artificial bee colony algorithm[C]// Proceeding of the 2018 Pacific-Asia Conference on Knowledge Discovery and Data Mining, LNCS 10939. Cham: Springer, 2018: 3-14.
13
SONG W, LI J Y, HUANG C M. Artificial fish swarm algorithm for mining high utility itemsets[C]// Proceeding of the 2021 International Conference on Swarm Intelligence, LNCS 12690. Cham: Springer, 2021: 407-419.
14
SONG W, HUANG C M. Mining high utility itemsets using bio-inspired algorithms: a diverse optimal value framework[J]. IEEE Access, 2018, 6: 19568-19582. 10.1109/access.2018.2819162
15
YANG X S. A new metaheuristic bat-inspired algorithm[M]// GONZÁLEZ J R, PELTA D A, CRUZ C, et al. Nature Inspired Cooperative Strategies for Optimization (NICSO 2010), SCI 284. Berlin: Springer, 2010: 65-74. 10.1007/978-3-642-12538-6_6